Training method and device of recommendation model, and recommendation method and device

Abstract

The invention provides a training method and device of a recommendation model, and a recommendation method and device, and the training method of a recommendation model comprises the steps: obtainingthe user characteristics of at least two sample users and the attribute characteristics of at least two sample application programs; generating a positive sample which is clicked and purchased by thesample user on the exposed sample application program and a negative sample which is clicked but not purchased or unclicked by the sample user on the exposed sample application program based on the user characteristics and the attribute characteristics; and training a recommendation model on the basis of a sample set comprising at least one positive sample and at least one negative sample to obtain the recommendation model, and outputting an exposure conversion rate obtained by each sample user on the basis of the click rate and the purchase rate of each exposed sample application program by the recommendation model.

Description

technical field [0001] The present application relates to the technical field of computer distributed processing, and in particular to a recommendation model training method and device, a recommendation method and device, a computing device, and a computer-readable storage medium. Background technique [0002] In some Internet products, in order to facilitate users to quickly find the products they want (such as APP), personalized recommendations are required, and the products that users are most likely to click to buy are recommended to users to be recommended. [0003] At present, some platforms simply use traditional machine learning models (such as LR or GBDT models) to recommend products based on user and APP tags when recommending products. This method of using tags for recommendation requires a lot of manpower to create tags system, and it is easy to recommend inappropriate apps to users (for example: apps for pornography, gambling and drugs), and the traditional mach...

AI Technical Summary

Problems solved by technology

[0003] At present, some platforms simply use traditional machine learning models (such as LR or GBDT models) to recommend products based on user and APP tags when recommending products. This method of using tags for recommendation requires a lot of manpower to create tags system, and it is easy to recommend inappropriate apps to users (for example: apps for pornography, gambling and drugs), and the traditional machine learning model only estimates the click-through rate of the app, and does not make full use of the purchase data after the click, and the click-through rate is high. APP, the conversion rate after clicking is not necessarily high

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